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Machine Learning Training Courses

Local, instructor-led live Machine Learning (ML) training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.
Machine Learning training is available as "onsite live training" or "remote live training". Onsite live Machine Learning trainings in Saudi Arabia can be carried out locally on customer premises or in NobleProg corporate training centers. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider

Testimonials

★★★★★

★★★★★

Ref material to use later was very good.

PAUL BEALES- Seagate Technology.

Course:Applied Machine Learning

What did you like the most about the training?:
Gave me good practice with using R to build machine learning systems for real situations. I can use this in my work straight away.
This was an excellent course. One of the best I have had.

Matthew Thomas - British Telecom

Course:Applied Machine Learning

It was very interactive and more relaxed and informal than expected. We covered lots of topics in the time and the trainer was always receptive to talking more in detail or more generally about the topics and how they were related. I feel the training has given me the tools to continue learning as opposed to it being a one off session where learning stops once you've finished which is very important given the scale and complexity of the topic.

Jonathan Blease

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

The trainer was so knowledgeable and included areas I was interested in.

Mohamed Salama

Course:Data Mining & Machine Learning with R

We have gotten a lot more insight in to the subject matter. Some nice discussion were made with some real subjects within our company.

Sebastiaan Holman

Course:Machine Learning and Deep Learning

The training provided the right foundation that allows us to further to expand on, by showing how theory and practice go hand in hand. It actually got me more interested in the subject than I was before.

The Jupyter notebook form, in which the training material is available

L M ERICSSON LIMITED

Course:Machine Learning

There were many exercises and interesting topics.

L M ERICSSON LIMITED

Course:Machine Learning

some great lab exercises analyzed and explained by the trainer in depth (e.g. covariants in linear regression, matching the real function)

L M ERICSSON LIMITED

Course:Machine Learning

It's just great that all material including the exercises is on the same page and then it gets updated on the fly. The solution is revealed at the end. Cool! Also, I do appreciate that Krzysztof took extra effort to understand our problems and suggested us possible techniques.

Attila Nagy - L M ERICSSON LIMITED

Course:Machine Learning

The trainer very easily explained difficult and advanced topics.

Leszek K

Course:Artificial Intelligence Overview

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All like it

蒙 李

Course:Machine Learning Fundamentals with Python

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way of conducting and example given by the trainer

ORANGE POLSKA S.A.

Course:Machine Learning and Deep Learning

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Possibility to discuss the proposed issues yourself

ORANGE POLSKA S.A.

Course:Machine Learning and Deep Learning

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Communication with lecturers

文欣 张

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

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like it all

lisa xie

Course:Artificial Neural Networks, Machine Learning, Deep Thinking

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Machine Learning Subcategories

Machine Learning Course Outlines

This course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.

TensorFlow Serving is a system for serving machine learning (ML) models to production.

In this instructor-led, live training, participants will learn how to configure and use TensorFlow Serving to deploy and manage ML models in a production environment.

By the end of this training, participants will be able to:

- Train, export and serve various TensorFlow models- Test and deploy algorithms using a single architecture and set of APIs- Extend TensorFlow Serving to serve other types of models beyond TensorFlow models

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP.

In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions.

In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.

In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.

Part-3(40%) of the training would be extensively based on Tensorflow - 2nd Generation API of Google's open source software library for Deep Learning. The examples and handson would all be made in TensorFlow.

Audience

This course is intended for engineers seeking to use TensorFlow for their Deep Learning projects

After completing this course, delegates will:

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have a good understanding on deep neural networks(DNN), CNN and RNN

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understand TensorFlow’s structure and deployment mechanisms

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be able to carry out installation / production environment / architecture tasks and configuration

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be able to assess code quality, perform debugging, monitoring

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be able to implement advanced production like training models, building graphs and logging

Not all the topics would be covered in a public classroom with 35 hours duration due to the vastness of the subject.

The Duration of the complete course will be around 70 hours and not 35 hours.

The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.

In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.

By the end of this training, participants will be able to:

- Install and configure OpenNLP- Download existing models as well as create their own- Train the models on various sets of sample data- Integrate OpenNLP with existing Java applications

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry. R will be used as the programming language.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of live projects.

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. R will be used as the programming language.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

By the end of this training, participants will be able to:

- Understand the fundamental concepts in machine learning- Learn the applications and uses of machine learning in finance- Develop their own algorithmic trading strategy using machine learning with R

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

In this instructor-led, live training, participants will learn how to create various neural network components using ENCOG. Real-world case studies will be discussed and machine language based solutions to these problems will be explored.

In this instructor-led, live training, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.

Embedding Projector is an open-source web application for visualizing the data used to train machine learning systems. Created by Google, it is part of TensorFlow.

This instructor-led, live training introduces the concepts behind Embedding Projector and walks participants through the setup of a demo project.

By the end of this training, participants will be able to:

- Explore how data is being interpreted by machine learning models- Navigate through 3D and 2D views of data to understand how a machine learning algorithm interprets it- Understand the concepts behind Embeddings and their role in representing mathematical vectors for images, words and numerals.- Explore the properties of a specific embedding to understand the behavior of a model- Apply Embedding Project to real-world use cases such building a song recommendation system for music lovers

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.

In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.

Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.

By the end of this training, participants will be able to:

- Understand the fundamental concepts in machine learning- Learn the applications and uses of machine learning in finance- Develop their own algorithmic trading strategy using machine learning with Python

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. R is a popular programming language in the financial industry. It is used in financial applications ranging from core trading programs to risk management systems.

In this instructor-led, live training, participants will learn how to implement deep learning models for finance using R as they step through the creation of a deep learning stock price prediction model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in finance- Use R to create deep learning models for finance- Build their own deep learning stock price prediction model using R

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.

In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model.

By the end of this training, participants will be able to:

- Understand the fundamental concepts of deep learning- Learn the applications and uses of deep learning in telecom- Use Python, Keras, and TensorFlow to create deep learning models for telecom- Build their own deep learning customer churn prediction model using Python

Audience

- Developers- Data scientists

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?

In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.

Audience

- City planners- Architects- Developers- Transportation officials

Format of the Course

- Part lecture, part discussion, and a series of interactive exercises.

Note

- To request a customized training for this course, please contact us to arrange.

Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep Learning is a subfield of Machine Learning which attempts to mimic the workings of the human brain in making decisions. It is trained with data in order to automatically provide solutions to problems. Deep Learning provides vast opportunities for the medical industry which is sitting on a data goldmine.

In this instructor-led, live training, participants will take part in a series of discussions, exercises and case-study analysis to understand the fundamentals of Deep Learning. The most important Deep Learning tools and techniques will be evaluated and exercises will be carried out to prepare participants for carrying out their own evaluation and implementation of Deep Learning solutions within their organizations.

By the end of this training, participants will be able to:

- Understand the fundamentals of Deep Learning- Learn Deep Learning techniques and their applications in the industry- Examine issues in medicine which can be solved by Deep Learning technologies- Explore Deep Learning case studies in medicine- Formulate a strategy for adopting the latest technologies in Deep Learning for solving problems in medicine

Audience

- Managers- Medical professionals in leadership roles

Format of the course

- Part lecture, part discussion, exercises and heavy hands-on practice

Note

- To request a customized training for this course, please contact us to arrange.